While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.
Fintech is becoming an increasingly competitive market. A KPMG analysis saw investments decline in 2016 and investors are now more cautious about betting on segments that are becoming saturated. Lending and payments are two segments that saw increased participation over the past two years. Competitors come in all forms. We
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Rapidly changing data management technology in the 21st Century has created cost-saving, business-expanding opportunities. Big companies around the world have realized the potential of modern technology to take over complex operations with more ease than a skilled worker could. There are debates about how it is pushing some traditional methods
During my years as a Consultant Data Scientist I have received many requests from my clients to provide frequency distribution reports for their specific business data needs. These reports have been very useful for the company management to make proper business decisions quickly. In this paper I would like to
R is ubiquitous in the machine learning community. Its ecosystem of more than 8,000 packages makes it the Swiss Army knife of modeling applications. Similarly, Apache Spark has rapidly become the big data platform of choice for data scientists. Its ability to perform calculations relatively quickly (due to features like in-memory
As the creation and consumption of data continues to grow among businesses of all sizes, so does the challenge of analyzing and turning that data into actionable insights. According to IBM, 90 percent of the data in the world today has been created in the last two years, at 2.5
Data processing today is done in form of pipelines which include various steps like aggregation, sanitization, filtering and finally generating insights by applying various statistical models. Amazon Kinesis is a platform to build pipelines for streaming data at the scale of terabytes per hour. Parts of the Kinesis platform are
Welcome to Part 2 of How to use Elasticsearch for Natural Language Processing and Text Mining. It’s been some time since Part 1, so you might want to brush up on the basics before getting started. This time we’ll focus on one very important type of query for Text Mining.
The R language is often perceived as a language for statisticians and data scientists. Quite a long time ago, this was mostly true. However, over the years the flexibility R provides via packages has made R into a more general purpose language. R was open sourced in 1995, and since
For people in the know, machine learning is old hat. Even so, it’s set to become the data buzzword of the year — for a rather mundane reason. When things get complex, people expect technology to ‘automagically’ solve the problem. Whether it’s automated financial product consultation or shopping in the supermarket of